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[BE] enable ruff rule RSE and remove useless parentheses in raise statements (#124261)

Browse Source 
Remove useless parentheses in `raise` statements if the exception type is raised with no argument.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/124261
Approved by: https://github.com/albanD
This commit is contained in:
Xuehai Pan 2024-04-17 19:29:30 +00:00 committed by PyTorch MergeBot
parent b726a23d4e
commit 93e249969b
98 changed files with 297 additions and 296 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
benchmarks/dynamo/common.py
View File

@ -1858,7 +1858,7 @@ class TimeOutException(Exception):
def alarm_handler(signum, frame):
raise TimeOutException()
raise TimeOutException
def exit_after(s):
@ -2136,7 +2136,7 @@ class BenchmarkRunner:
return set()
def get_tolerance_and_cosine_flag(self, is_training, current_device, name):
raise NotImplementedError()
raise NotImplementedError
@property
def equal_nan(self):

2
benchmarks/dynamo/microbenchmarks/operator_inp_utils.py
View File

@ -80,7 +80,7 @@ def serialize_sparse_tensor(e):
def deserialize_sparse_tensor(size, dtype, layout, is_coalesced, nnz=None):
raise NotImplementedError()
raise NotImplementedError
def deserialize_tensor(size, dtype, stride=None):

1
pyproject.toml
View File

@ -126,6 +126,7 @@ select = [
"PT025",
"PT026",
"PYI",
"RSE",
"RUF008", # mutable dataclass default
"RUF015", # access first ele in constant time
"RUF016", # type error non-integer index

2
scripts/release_notes/commitlist.py
View File

@ -578,7 +578,7 @@ def main():
with open(filename, "w") as f:
f.writelines(lines)
return
raise AssertionError()
raise AssertionError
if __name__ == "__main__":

4
test/cpp/jit/tests_setup.py
View File

@ -6,10 +6,10 @@ import torch
class Setup:
def setup(self):
raise NotImplementedError()
raise NotImplementedError
def shutdown(self):
raise NotImplementedError()
raise NotImplementedError
class FileSetup:

8
test/distributed/checkpoint/e2e/test_fsdp_ep.py
View File

@ -21,7 +21,7 @@ class Dummymodel(nn.Module):
super().__init__()
def forward(self, x):
raise NotImplementedError()
raise NotImplementedError
class EPModel(nn.Module):
@ -31,7 +31,7 @@ class EPModel(nn.Module):
self.net2 = nn.Sequential(nn.Linear(16, 16), nn.ReLU())
def forward(self, x):
raise NotImplementedError()
raise NotImplementedError
class SecondTier(nn.Module):
@ -43,7 +43,7 @@ class SecondTier(nn.Module):
self.net = nn.Sequential(nn.Linear(16, 16), nn.ReLU())
def forward(self, x):
raise NotImplementedError()
raise NotImplementedError
class TopModel(nn.Module):
@ -55,7 +55,7 @@ class TopModel(nn.Module):
self.net = nn.Sequential(nn.Linear(16, 16), nn.ReLU())
def forward(self, x):
raise NotImplementedError()
raise NotImplementedError
class TestFSDPWithEP(DTensorTestBase, VerifyStateDictMixin):

4
test/distributed/elastic/metrics/api_test.py
View File

@ -34,7 +34,7 @@ class TestMetricsHandler(MetricHandler):
class Parent(abc.ABC):
@abc.abstractmethod
def func(self):
raise NotImplementedError()
raise NotImplementedError
def base_func(self):
self.func()
@ -57,7 +57,7 @@ class MetricsApiTest(TestCase):
@prof
def throw(self):
raise RuntimeError()
raise RuntimeError
@prof(group="torchelastic")
def bar2(self):

2
test/distributed/elastic/rendezvous/api_test.py
View File

@ -197,7 +197,7 @@ class _DummyRendezvousHandler(RendezvousHandler):
return "dummy_backend"
def next_rendezvous(self) -> Tuple[Store, int, int]:
raise NotImplementedError()
raise NotImplementedError
def is_closed(self) -> bool:
return False

2
test/distributed/elastic/utils/distributed_test.py
View File

@ -38,7 +38,7 @@ def _create_c10d_store_mp(is_server, server_addr, port, world_size, wait_for_wor
timeout=2,
)
if store is None:
raise AssertionError()
raise AssertionError
store.set(f"test_key/{os.getpid()}", b"test_value")

2
test/distributed/fsdp/test_fsdp_optim_state.py
View File

@ -363,7 +363,7 @@ class TestFSDPOptimState(FSDPTest):
# these settings are not implemented since the transformer is
# wrapped with FSDP at the top-level, which means that there is
# only a single flat parameter, making these booleans vacuous
raise NotImplementedError()
raise NotImplementedError
if group is None:
group = dist.distributed_c10d._get_default_group()
model = TransformerWithSharedParams.init(

2
test/distributed/pipeline/sync/test_bugs.py
View File

@ -63,7 +63,7 @@ def test_exception_no_hang(setup_rpc):
class Raise(nn.Module):
def forward(self, x):
raise ExpectedException()
raise ExpectedException
model = nn.Sequential(Pass(), Pass(), Raise())
model = Pipe(model, chunks=3)

4
test/distributed/pipeline/sync/test_pipe.py
View File

@ -231,7 +231,7 @@ def test_exception(setup_rpc):
class Raise(nn.Module):
def forward(self, *_):
raise ExpectedException()
raise ExpectedException
model = nn.Sequential(Raise())
model = Pipe(model, chunks=1)
@ -265,7 +265,7 @@ def test_exception_early_stop_asap(setup_rpc):
class Raise(nn.Module):
def forward(self, x):
raise ExpectedException()
raise ExpectedException
model = nn.Sequential(Pass(), Pass(), Counter(), Raise())
model = Pipe(model, chunks=3)

2
test/dynamo/test_autograd_function.py
View File

@ -752,7 +752,7 @@ class AutogradFunctionTests(torch._dynamo.test_case.TestCase):
new_data = args[0]._data.view(*args[1:])
return FooTensor(new_data, args[0]._config, args[0]._scale)
raise NotImplementedError()
raise NotImplementedError
class foo_autograd_fn(torch.autograd.Function):
@staticmethod

10
test/dynamo/test_exc.py
View File

@ -47,7 +47,7 @@ from user code:
def test_internal_error_suppress_errors(self, records):
def fn001(x):
def f(ctx):
raise AssertionError()
raise AssertionError
comptime(f)
@ -62,7 +62,7 @@ WON'T CONVERT fn001 test_exc.py line N
========== TorchDynamo Stack Trace ==========
Traceback (most recent call last):
File "test_exc.py", line N, in f
raise AssertionError()
raise AssertionError
AssertionError:
from user code:
@ -84,7 +84,7 @@ from user code:
def test_not_implemented_error(self, records):
def fn001(x):
def f(ctx):
raise NotImplementedError()
raise NotImplementedError
# Ensure graph break is not possible
for i in range(3):
@ -101,7 +101,7 @@ WON'T CONVERT fn001 test_exc.py line N
due to:
Traceback (most recent call last):
File "test_exc.py", line N, in f
raise NotImplementedError()
raise NotImplementedError
torch._dynamo.exc.InternalTorchDynamoError:
from user code:
@ -128,7 +128,7 @@ from user code:
# NB: avoid decorator, as 3.11 changed the line number attributed
# in this situation
def f(ctx):
raise AssertionError()
raise AssertionError
comptime(f)

4
test/dynamo/test_logging.py
View File

@ -164,7 +164,7 @@ from user code:
import torch._inductor.lowering
def throw(x):
raise AssertionError()
raise AssertionError
# inject an error in the lowerings
dict_entries = {}
@ -189,7 +189,7 @@ WON'T CONVERT inductor_error_fn test_logging.py line N
due to:
Traceback (most recent call last):
File "test_logging.py", line N, in throw
raise AssertionError()
raise AssertionError
torch._dynamo.exc.BackendCompilerFailed: backend='inductor' raised:
LoweringException: AssertionError:
target: aten.round.default

12
test/dynamo/test_repros.py
View File

@ -396,12 +396,12 @@ class ListConfig:
if self.resolve:
x = x._dereference_node()
if x._is_missing():
raise AssertionError()
raise AssertionError
self.index = self.index + 1
if isinstance(x, ListConfig.ValueNode):
return x._value()
raise AssertionError()
raise AssertionError
def __iter__(self):
return self._iter_ex(True)
@ -410,7 +410,7 @@ class ListConfig:
try:
return ListConfig.ListIterator(self, resolve)
except Exception:
raise AssertionError()
raise AssertionError
def __init__(self):
self._content = [
@ -545,7 +545,7 @@ def apply_chunking_to_forward(forward_fn, *input_tensors):
assert all(input_tensor.shape[1] == tensor_shape for input_tensor in input_tensors)
num_args_in_forward_chunk_fn = len(inspect.signature(forward_fn).parameters)
if num_args_in_forward_chunk_fn != len(input_tensors):
raise ValueError()
raise ValueError
return forward_fn(*input_tensors)
@ -848,7 +848,7 @@ def _merge_criteria_processor_list(default_list, custom_list):
for default in default_list:
for custom in custom_list:
if type(custom) is type(default):
raise ValueError()
raise ValueError
default_list.extend(custom_list)
return default_list
@ -2573,7 +2573,7 @@ class ReproTests(torch._dynamo.test_case.TestCase):
if self.i < 3:
self.i += 1
return self.i
raise StopIteration()
raise StopIteration
@torch.compile(backend="eager", fullgraph=True)
def fn(x):

4
test/dynamo/test_skip_non_tensor.py
View File

@ -147,10 +147,10 @@ class SkipNonTensorTests(torch._dynamo.test_case.TestCase):
class Foo(list):
def __iter__(self):
raise Exception()
raise Exception
def __len__(self):
raise Exception()
raise Exception
x = Foo()
x.append(torch.randn(4))

2
test/dynamo/test_structured_trace.py
View File

@ -233,7 +233,7 @@ class StructuredTraceTest(TestCase):
import torch._inductor.lowering
def throw(x):
raise AssertionError()
raise AssertionError
# inject an error in the lowerings
dict_entries = {}

6
test/functorch/discover_coverage.py
View File

@ -732,12 +732,12 @@ class Operator:
def any_opinfo_attr(self, attr):
if not self.has_opinfo():
raise RuntimeError()
raise RuntimeError
return any(getattr(opinfo, attr) for opinfo in self.opinfos)
def all_opinfo_attr(self, attr):
if not self.has_opinfo():
raise RuntimeError()
raise RuntimeError
return all(getattr(opinfo, attr) for opinfo in self.opinfos)
def supports_vjp(self):
@ -870,7 +870,7 @@ class OperatorSet:
elif n.startswith(torch_dot):
names_sanitized.append(n[len(torch_dot) :])
else:
raise AssertionError()
raise AssertionError
return cls.from_names(names_sanitized)
def query(self, operator_method, filter=(Support.NO, Support.YES, Support.UNKNOWN)):

4
test/functorch/test_dims.py
View File

@ -403,7 +403,7 @@ class TestMin(TestCase):
# test with too many elements
try:
A[1, ..., 1, 1]
raise NotImplementedError()
raise NotImplementedError
except IndexError:
pass
c, d = dims()
@ -415,7 +415,7 @@ class TestMin(TestCase):
)
try:
A[..., 3, ...]
raise NotImplementedError()
raise NotImplementedError
except DimensionBindError:
pass

4
test/inductor/test_torchinductor_dynamic_shapes.py
View File

@ -285,7 +285,7 @@ class TestInductorDynamic(TestCase):
@custom_ops.custom_op("test::foo")
def foo(x: torch.Tensor, y: int) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl("test::foo")
def foo_impl(x: torch.Tensor, y: int) -> torch.Tensor:
@ -401,7 +401,7 @@ class TestInductorDynamic(TestCase):
@custom_ops.custom_op("test::foo")
def foo(x: torch.Tensor) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl("test::foo")
def foo_impl(x: torch.Tensor) -> torch.Tensor:

2
test/jit/test_freezing.py
View File

@ -2632,7 +2632,7 @@ class TestFrozenOptimizations(JitTestCase):
res3 = torch._C._nn.linear(in_tensor2, self.w2, self.b2)
res4 = torch._C._nn.linear(in_tensor1, self.w2, self.b1)
else:
raise AssertionError()
raise AssertionError
res2 = torch._C._nn.linear(in_tensor1, self.w2, self.b1)
return res1, res2, res3, res4

2
test/jit/test_list_dict.py
View File

@ -2871,7 +2871,7 @@ class TestScriptList(JitTestCase):
def __next__(self):
if self.value == limit: # noqa: F821
raise StopIteration()
raise StopIteration
ret = self.value
self.value += 1

4
test/lazy/test_step_closures.py
View File

@ -54,7 +54,7 @@ class ClosuresTest(TestCase):
torch._lazy.add_step_closure(closure)
torch._lazy.mark_step()
raise AssertionError() # Should not reach here
raise AssertionError # Should not reach here
except RuntimeError as e:
assert flag.is_set(), "Should have caught exception from closure"
@ -79,7 +79,7 @@ class ClosuresTest(TestCase):
torch._lazy.add_step_closure(closure2, run_async=True)
torch._lazy.mark_step()
raise AssertionError() # Should not reach here
raise AssertionError # Should not reach here
except RuntimeError as e:
# Should have caught exception from closure1
pass

2
test/onnx/test_operators.py
View File

@ -283,7 +283,7 @@ class TestOperators(common_utils.TestCase):
def symbolic(g, x):
# The inside of this function should never be invoked, because
# we will fail due to an argument mismatch first.
raise AssertionError()
raise AssertionError
@staticmethod
def forward(ctx, x, y):

4
test/onnx/verify.py
View File

@ -154,7 +154,7 @@ class Errors:
NB: It is an error to "fail" without having added any errors to
the error context.
"""
raise self.exc_class()
raise self.exc_class
def failWith(self, msg):
"""
@ -489,7 +489,7 @@ def verify(
errs.requireEqual(
proto_bytes.getvalue(), alt_proto_bytes.getvalue()
)
raise AssertionError()
raise AssertionError
# TODO: test that the traced model also returns the same thing...
run_helper(torch_out, args, remained_onnx_input_idx)

2
test/package/test_importer.py
View File

@ -98,7 +98,7 @@ class TestImporter(PackageTestCase):
self._whichmodule_return = whichmodule_return
def import_module(self, module_name):
raise NotImplementedError()
raise NotImplementedError
def whichmodule(self, obj, name):
return self._whichmodule_return

2
test/profiler/test_profiler.py
View File

@ -1970,7 +1970,7 @@ assert KinetoStepTracker.current_step() == initial_step + 2 * niters
try:
with cm:
x.add(y)
raise ValueError()
raise ValueError
x.relu()
except ValueError:
pass

94
test/test_custom_ops.py
View File

@ -509,7 +509,7 @@ class TestCustomOp(CustomOpTestCaseBase):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def baz(x: Tensor) -> Tensor:
raise NotImplementedError()
raise NotImplementedError
def test_unsupported_schemas(self):
with self.assertRaisesRegex(ValueError, "only supports functional"):
@ -670,35 +670,35 @@ class TestCustomOp(CustomOpTestCaseBase):
with self.assertRaisesRegex(ValueError, "varargs"):
def foo(*args):
raise NotImplementedError()
raise NotImplementedError
infer_schema(foo)
with self.assertRaisesRegex(ValueError, "varkwargs"):
def foo(**kwargs):
raise NotImplementedError()
raise NotImplementedError
infer_schema(foo)
with self.assertRaisesRegex(ValueError, "must have a type annotation"):
def foo(x):
raise NotImplementedError()
raise NotImplementedError
infer_schema(foo)
with self.assertRaisesRegex(ValueError, "unsupported"):
def foo(x: Tensor) -> Tuple[Tensor, ...]:
raise NotImplementedError()
raise NotImplementedError
infer_schema(foo)
with self.assertRaisesRegex(ValueError, "can be mutated"):
def foo(x: Tensor, y: int) -> Tensor:
raise NotImplementedError()
raise NotImplementedError
infer_schema(foo, mutates_args={"y"})
@ -752,7 +752,7 @@ class TestCustomOp(CustomOpTestCaseBase):
@custom_ops.custom_op(f"{self.test_ns}::foo")
def foo(x: Tensor) -> typ:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl(f"{self.test_ns}::foo")
def foo_impl(x: Tensor) -> typ:
@ -771,7 +771,7 @@ class TestCustomOp(CustomOpTestCaseBase):
@custom_ops.custom_op(f"{self.test_ns}::foo")
def foo(x: Tensor) -> Tuple[typ, typ]:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl(f"{self.test_ns}::foo")
def foo_impl(x: Tensor) -> Tuple[typ, typ]:
@ -789,7 +789,7 @@ class TestCustomOp(CustomOpTestCaseBase):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: Tensor, y: typ) -> Tensor:
raise NotImplementedError()
raise NotImplementedError
yeet = None
@ -823,7 +823,7 @@ class TestCustomOp(CustomOpTestCaseBase):
@custom_ops.custom_op(f"{self.test_ns}::foo")
def foo(x: torch.Tensor, sizes: Sequence[int]) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
called = 0
@ -847,7 +847,7 @@ class TestCustomOp(CustomOpTestCaseBase):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: Tensor, y: List[Optional[int]]) -> Tensor:
raise NotImplementedError()
raise NotImplementedError
del foo
@ -855,7 +855,7 @@ class TestCustomOp(CustomOpTestCaseBase):
# int[N] in Dispatcher is a bit wild, so we don't try to support it.
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: Tensor, y: Tuple[int, int]) -> Tensor:
raise NotImplementedError()
raise NotImplementedError
del foo
@ -863,7 +863,7 @@ class TestCustomOp(CustomOpTestCaseBase):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: Tensor, y: Callable) -> Tensor:
raise NotImplementedError()
raise NotImplementedError
del foo
@ -910,7 +910,7 @@ class TestCustomOp(CustomOpTestCaseBase):
@custom_ops.custom_op(f"{ns}::foo2")
def foo2(x: torch.Tensor) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
def test_private_ctor(self):
with self.assertRaisesRegex(RuntimeError, "CustomOp constructor is private"):
@ -919,7 +919,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_lifetime(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
custom_op = torch._custom_op.impl.get_op(f"{TestCustomOp.test_ns}::foo")
@ -928,7 +928,7 @@ class TestCustomOp(CustomOpTestCaseBase):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor) -> torch.Tensor: # noqa: F811
raise NotImplementedError()
raise NotImplementedError
# Unless we delete the original op.
custom_ops._destroy(f"{TestCustomOp.test_ns}::foo")
@ -936,14 +936,14 @@ class TestCustomOp(CustomOpTestCaseBase):
# Smoke test
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor) -> torch.Tensor: # noqa: F811
raise NotImplementedError()
raise NotImplementedError
custom_ops._destroy(f"{TestCustomOp.test_ns}::foo")
def test_autograd_notimplemented(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor) -> torch.Tensor: # noqa: F811
raise NotImplementedError()
raise NotImplementedError
x = torch.randn(3, requires_grad=True)
op = self.get_op(f"{self.test_ns}::foo")
@ -954,7 +954,7 @@ class TestCustomOp(CustomOpTestCaseBase):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: Sequence[torch.Tensor]) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
x = torch.randn(3, requires_grad=True)
y = torch.randn(3)
@ -966,7 +966,7 @@ class TestCustomOp(CustomOpTestCaseBase):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
x = torch.randn(3, requires_grad=True)
y = torch.randn(3)
@ -978,7 +978,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_autograd_notimplemented_gradmode(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl(f"{TestCustomOp.test_ns}::foo")
def foo_impl(x, y):
@ -994,7 +994,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_impl_cpu(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl(f"{TestCustomOp.test_ns}::foo", device_types="cpu")
def foo_cpu(x):
@ -1008,7 +1008,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_impl_invalid_devices(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
def foo_impl(x):
return x.sin()
@ -1033,7 +1033,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_backward_partially_registered(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl(f"{TestCustomOp.test_ns}::foo")
def foo_impl(x):
@ -1054,7 +1054,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_save_for_backward_inputs_are_namedtuple(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl(f"{TestCustomOp.test_ns}::foo")
def foo_impl(x):
@ -1084,7 +1084,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_backward_returns_dict(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl(f"{TestCustomOp.test_ns}::foo")
def foo_impl(x):
@ -1107,7 +1107,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_backward_dict_invalid_keys(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl(f"{TestCustomOp.test_ns}::foo")
def foo_impl(x):
@ -1130,7 +1130,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_backward_dict_grad_for_nontensor(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor, dim: int) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl(f"{TestCustomOp.test_ns}::foo")
def foo_impl(x, dim):
@ -1153,7 +1153,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_backward_dict_requires_keys_for_input_tensors(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl(f"{TestCustomOp.test_ns}::foo")
def foo_impl(x, y):
@ -1176,7 +1176,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_backward_dict_requires_keys_for_input_optional_tensors(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor, y: Optional[torch.Tensor]) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl(f"{TestCustomOp.test_ns}::foo")
def foo_impl(x, y):
@ -1199,7 +1199,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_backward_grads_are_tensor_or_none(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl(f"{TestCustomOp.test_ns}::foo")
def foo_impl(x):
@ -1222,7 +1222,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_backward_tensorlist_input_requires_list_grads_with_same_numel(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(xs: Sequence[torch.Tensor]) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl(f"{TestCustomOp.test_ns}::foo")
def foo_impl(xs):
@ -1245,7 +1245,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_backward_tensorlist_input_requires_list_grads_none_or_Tensor(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(xs: Sequence[torch.Tensor]) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl(f"{TestCustomOp.test_ns}::foo")
def foo_impl(xs):
@ -1268,7 +1268,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_backward_tensorlist_input_requires_list_grads(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(xs: Sequence[torch.Tensor]) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl(f"{TestCustomOp.test_ns}::foo")
def foo_impl(xs):
@ -1291,7 +1291,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_backward_output_differentiability_type(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(xs: Sequence[torch.Tensor]) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
with self.assertRaisesRegex(RuntimeError, "output_differentiability"):
@ -1304,7 +1304,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_backward_output_differentiability_numel(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(xs: Sequence[torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
raise NotImplementedError()
raise NotImplementedError
with self.assertRaisesRegex(RuntimeError, "output_differentiability"):
@ -1317,7 +1317,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_backward_output_differentiability_tensorlist(self):
@custom_ops.custom_op(f"{self.test_ns}::foo")
def foo(x: Tensor) -> Tuple[List[Tensor], Tensor]:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl(f"{self.test_ns}::foo")
def foo_impl(x):
@ -1343,7 +1343,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_backward_output_differentiability_non_tensor(self):
@custom_ops.custom_op(f"{self.test_ns}::foo")
def foo(x: Tensor) -> Tuple[Tensor, int]:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl(f"{self.test_ns}::foo")
def foo_impl(x):
@ -1368,7 +1368,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_impl_separate(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl(f"{TestCustomOp.test_ns}::foo", device_types="cpu")
def foo_cpu(x):
@ -1392,7 +1392,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_impl_multiple(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@custom_ops.impl(f"{TestCustomOp.test_ns}::foo")
def foo_impl(x):
@ -1422,7 +1422,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_impl_meta(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor, dim: int) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@torch.library.impl_abstract(f"{TestCustomOp.test_ns}::foo", lib=self.lib())
def foo_meta(x, dim):
@ -1438,7 +1438,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_duplicate_impl(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor, dim: int) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@torch.library.impl_abstract(f"{TestCustomOp.test_ns}::foo", lib=self.lib())
def foo_meta(x, dim):
@ -1457,7 +1457,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_new_data_dependent_symint(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@torch.library.impl_abstract(f"{TestCustomOp.test_ns}::foo", lib=self.lib())
def foo_meta(x):
@ -1483,7 +1483,7 @@ class TestCustomOp(CustomOpTestCaseBase):
# this one is just a sanity check.
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
@torch.library.impl_abstract(f"{TestCustomOp.test_ns}::foo", lib=self.lib())
def foo_meta(x):
@ -1497,7 +1497,7 @@ class TestCustomOp(CustomOpTestCaseBase):
def test_not_implemented_error(self):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::foo")
def foo(x: torch.Tensor) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
x = torch.randn(3)
op = self.get_op(f"{self.test_ns}::foo")
@ -1510,7 +1510,7 @@ class TestCustomOp(CustomOpTestCaseBase):
@custom_ops.custom_op(f"{TestCustomOp.test_ns}::bar")
def bar(sizes: Sequence[int]) -> torch.Tensor:
raise NotImplementedError()
raise NotImplementedError
op = self.get_op(f"{self.test_ns}::bar")
with self.assertRaisesRegex(NotImplementedError, "no Tensor inputs"):
@ -2021,7 +2021,7 @@ class MiniOpTest(CustomOpTestCaseBase):
@staticmethod
def backward(ctx, grad):
raise NotImplementedError()
raise NotImplementedError
def autograd_impl(x):
return Op.apply(x)

2
test/test_maskedtensor.py
View File

@ -492,7 +492,7 @@ class TestBinary(TestCase):
mt1 = masked_tensor(data1, mask1)
try:
fn(mt0, mt1)
raise AssertionError()
raise AssertionError
except ValueError as e:
assert (
"Input masks must match. If you need support for this, please open an issue on Github."

2
test/test_nn.py
View File

@ -6603,7 +6603,7 @@ tensor(..., device='meta', size=(1,), requires_grad=True)""")
elif weight_layout == torch.sparse_coo:
module.weight = nn.Parameter(module.weight.to_sparse_coo())
else:
raise AssertionError()
raise AssertionError
inp = torch.randn(4, requires_grad=True, device=device)
res = module(inp)

4
test/test_overrides.py
View File

@ -1206,7 +1206,7 @@ class TestTorchFunctionMode(TestCase):
class A(TorchFunctionMode):
def __torch_function__(self, *args, **kwargs):
raise ErrorA()
raise ErrorA
with self.assertRaises(ErrorA):
with A():
@ -1218,7 +1218,7 @@ class TestTorchFunctionMode(TestCase):
class A(TorchFunctionMode):
def __torch_function__(self, *args, **kwargs):
raise ErrorA()
raise ErrorA
x = A()
with self.assertRaises(ErrorA):

4
test/test_python_dispatch.py
View File

@ -1238,7 +1238,7 @@ $3: f32[] = torch._ops.aten.add.Tensor($1, $2)""")
class AMode(TorchDispatchMode):
def __torch_dispatch__(self, func, types, args=(), kwargs=None):
if func.__name__ == 'randn.default':
raise RuntimeError()
raise RuntimeError
return A(torch.zeros(()))
with AMode():
@ -1254,7 +1254,7 @@ $3: f32[] = torch._ops.aten.add.Tensor($1, $2)""")
class A(TorchDispatchMode):
def __torch_dispatch__(self, func, types, args=(), kwargs=None):
raise ErrorA()
raise ErrorA
x = A()
with self.assertRaises(ErrorA):

4
test/test_utils.py
View File

@ -1081,7 +1081,7 @@ class TestTraceback(TestCase):
source = '''\
def f(x):
def g(x):
raise RuntimeError() # HEYA
raise RuntimeError # HEYA
x = x * 3
return g(x) + 1
@ -1099,7 +1099,7 @@ def f(x):
def test_format_traceback_short(self):
try:
raise RuntimeError()
raise RuntimeError
except RuntimeError as e:
self.assertRegex(format_traceback_short(e.__traceback__), r'.*test_utils.py:\d+ in test_format_traceback_short')

4
test/test_weak.py
View File

@ -525,7 +525,7 @@ class WeakKeyDictionaryTestCase(TestCase):
return self
def __next__(self):
raise Exc()
raise Exc
self.assertRaises(Exc, d.update, badseq())
@ -866,7 +866,7 @@ class WeakKeyDictionaryScriptObjectTestCase(TestCase):
return self
def __next__(self):
raise Exc()
raise Exc
self.assertRaises(Exc, d.update, badseq())

6
test/torch_np/numpy_tests/core/test_multiarray.py
View File

@ -1136,14 +1136,14 @@ class TestCreation(TestCase):
return 1
def __getitem__(self, index):
raise ValueError()
raise ValueError
class Map:
def __len__(self):
return 1
def __getitem__(self, index):
raise KeyError()
raise KeyError
a = np.array([Map()])
assert_(a.shape == (1,))
@ -1160,7 +1160,7 @@ class TestCreation(TestCase):
if ind in [0, 1]:
return ind
else:
raise IndexError()
raise IndexError
d = np.array([Point2(), Point2(), Point2()])
assert_equal(d.dtype, np.dtype(object))

2
test/torch_np/numpy_tests/fft/test_pocketfft.py
View File

@ -342,7 +342,7 @@ class TestFFT1D(TestCase):
Y_res = fft(Y, axes=ax)
assert_allclose(X_res, Y_res, atol=_tol, rtol=_tol)
else:
raise ValueError()
raise ValueError
@skipif(IS_WASM, reason="Cannot start thread")

2
test/torch_np/numpy_tests/lib/test_function_base.py
View File

@ -1435,7 +1435,7 @@ class TestVectorize(TestCase):
try:
vectorize(random.randrange) # Should succeed
except Exception:
raise AssertionError() # noqa: TRY200
raise AssertionError # noqa: TRY200
def test_keywords2_ticket_2100(self):
# Test kwarg support: enhancement ticket 2100

2
tools/autograd/gen_variable_type.py
View File

@ -1252,7 +1252,7 @@ def emit_body(
if a.name == derivative_var_name:
break
else:
raise AssertionError()
raise AssertionError
return f"grad_fn->should_compute_output({edge_off})"
if is_inplace_foreach:

6
torch/_custom_ops.py
View File

@ -61,7 +61,7 @@ def custom_op(qualname, func_or_schema=None):
>>> # we will infer the types of the inputs and outputs.
>>> @torch._custom_ops.custom_op("mylibrary::numpy_sin")
>>> def numpy_sin(x: Tensor) -> Tensor:
>>> raise NotImplementedError()
>>> raise NotImplementedError
>>>
>>> # The custom op is now accessible via the torch.ops module:
>>> torch.ops.mylibrary.numpy_sin
@ -143,7 +143,7 @@ def impl(qualname, *, device_types=("cpu", "cuda"), func=None):
>>> # we will infer the types of the inputs and outputs.
>>> @torch._custom_ops.custom_op("mylibrary::numpy_cos")
>>> def numpy_cos(x: Tensor) -> Tensor:
>>> raise NotImplementedError()
>>> raise NotImplementedError
>>>
>>> # The custom op is now accessible via the torch.ops module:
>>> torch.ops.mylibrary.numpy_cos
@ -207,7 +207,7 @@ def impl_abstract(qualname, *, func=None):
>>> # Example 1: an operator without data-dependent output shape
>>> @torch._custom_ops.custom_op("mylibrary::custom_linear")
>>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor:
>>> raise NotImplementedError()
>>> raise NotImplementedError
>>>
>>> @torch._custom_ops.impl_abstract("mylibrary::custom_linear")
>>> def custom_linear_abstract(x, weight):

4
torch/_dynamo/backends/debugging.py
View File

@ -129,7 +129,7 @@ class TestingOnlyCompileError(Exception):
def relu_compile_error_TESTING_ONLY(gm: torch.fx.GraphModule, example_inputs):
for node in gm.graph.nodes:
if node.target == torch.relu:
raise ReluCompileError()
raise ReluCompileError
return gm
@ -165,7 +165,7 @@ def non_leaf_compile_error_TESTING_ONLY(gm: torch.fx.GraphModule, example_inputs
return gm
for t in example_inputs:
if not t.is_leaf:
raise TestingOnlyCompileError()
raise TestingOnlyCompileError
return gm

36
torch/_dynamo/device_interface.py
View File

@ -39,7 +39,7 @@ class DeviceInterface(metaclass=DeviceInterfaceMeta):
class device:
def __new__(cls, device: _device_t):
raise NotImplementedError()
raise NotImplementedError
class Worker:
"""
@ -51,71 +51,71 @@ class DeviceInterface(metaclass=DeviceInterfaceMeta):
@staticmethod
def set_device(device: int):
raise NotImplementedError()
raise NotImplementedError
@staticmethod
def current_device() -> int:
raise NotImplementedError()
raise NotImplementedError
@staticmethod
def get_device_properties(device: _device_t = None):
raise NotImplementedError()
raise NotImplementedError
@staticmethod
def current_device():
raise NotImplementedError()
raise NotImplementedError
@staticmethod
def set_device(device: _device_t):
raise NotImplementedError()
raise NotImplementedError
@staticmethod
def maybe_exchange_device(device: int) -> int:
raise NotImplementedError()
raise NotImplementedError
@staticmethod
def exchange_device(device: int) -> int:
raise NotImplementedError()
raise NotImplementedError
@staticmethod
def device_count():
raise NotImplementedError()
raise NotImplementedError
@staticmethod
def is_available() -> bool:
raise NotImplementedError()
raise NotImplementedError
@staticmethod
def stream(stream: torch.Stream):
raise NotImplementedError()
raise NotImplementedError
@staticmethod
def current_stream():
raise NotImplementedError()
raise NotImplementedError
@staticmethod
def set_stream(stream: torch.Stream):
raise NotImplementedError()
raise NotImplementedError
@staticmethod
def _set_stream_by_id(stream_id: int, device_index: int, device_type: int):
raise NotImplementedError()
raise NotImplementedError
@staticmethod
def get_raw_stream():
raise NotImplementedError()
raise NotImplementedError
@staticmethod
def synchronize(device: _device_t = None):
raise NotImplementedError()
raise NotImplementedError
@staticmethod
def get_device_properties(device: _device_t = None):
raise NotImplementedError()
raise NotImplementedError
@staticmethod
def get_compute_capability(device: _device_t = None):
raise NotImplementedError()
raise NotImplementedError
class DeviceGuard:

6
torch/_dynamo/source.py
View File

@ -215,7 +215,7 @@ class EphemeralSource(Source):
return f"<ephemeral{': ' + self.desc if self.desc is not None else ''}>"
def make_guard(self):
raise NotImplementedError()
raise NotImplementedError
def is_ephemeral(self):
return True
@ -277,7 +277,7 @@ class NegateSource(ChainedSource):
assert self.base is not None
def reconstruct(self, codegen):
raise NotImplementedError()
raise NotImplementedError
def guard_source(self):
return self.base.guard_source()
@ -516,7 +516,7 @@ class ConstantSource(Source):
return self.source_name
def make_guard(self, fn):
raise NotImplementedError()
raise NotImplementedError
@dataclasses.dataclass(frozen=True)

12
torch/_dynamo/symbolic_convert.py
View File

@ -1242,7 +1242,7 @@ class InstructionTranslatorBase(
if (
isinstance(val, BuiltinVariable) and val.fn is StopIteration
) or isinstance(val, variables.StopIterationVariable):
raise exc.UserStopIteration()
raise exc.UserStopIteration
unimplemented(f"raise {exc}")
else:
unimplemented("raise ... from ...")
@ -2231,7 +2231,7 @@ class InstructionTranslator(InstructionTranslatorBase):
return self.f_locals[source.local_name]
if isinstance(source, GlobalSource):
return self.f_globals[source.global_name]
raise KeyError()
raise KeyError
def run(self):
super().run()
@ -2388,7 +2388,7 @@ class InstructionTranslator(InstructionTranslatorBase):
else create_instruction("RETURN_CONST", argval=inst.argval)
)
self.output.add_output_instructions([return_inst])
raise ReturnValueOp()
raise ReturnValueOp
def RETURN_VALUE(self, inst):
self._return(inst)
@ -2637,7 +2637,7 @@ class InliningInstructionTranslator(InstructionTranslatorBase):
self.output.root_tx.mutated_closure_cell_contents.add(
maybe_cell.source.name()
)
raise exc.UnspecializeRestartAnalysis()
raise exc.UnspecializeRestartAnalysis
unimplemented("write to __closure__ while inlining")
def LOAD_DEREF(self, inst):
@ -2676,12 +2676,12 @@ class InliningInstructionTranslator(InstructionTranslatorBase):
def RETURN_VALUE(self, inst):
self.symbolic_result = self.pop() # type: ignore[assignment]
self.instruction_pointer = None
raise ReturnValueOp()
raise ReturnValueOp
def RETURN_CONST(self, inst):
self.symbolic_result = self._load_const(inst)
self.instruction_pointer = None
raise ReturnValueOp()
raise ReturnValueOp
class InliningGeneratorInstructionTranslator(InliningInstructionTranslator):

10
torch/_dynamo/variables/base.py
View File

@ -219,17 +219,17 @@ class VariableTracker(metaclass=VariableTrackerMeta):
def make_guard(self, fn):
if self.source:
return self.source.make_guard(fn)
raise NotImplementedError()
raise NotImplementedError
def const_getattr(self, tx, name: str) -> Any:
"""getattr(self, name) returning a python constant"""
raise NotImplementedError()
raise NotImplementedError
def var_getattr(self, tx, name: str) -> "VariableTracker":
"""getattr(self, name) returning a new variable"""
value = self.const_getattr(tx, name)
if not variables.ConstantVariable.is_literal(value):
raise NotImplementedError()
raise NotImplementedError
source = None
if self.source:
source = AttrSource(self.source, name)
@ -257,7 +257,7 @@ class VariableTracker(metaclass=VariableTrackerMeta):
return None
def reconstruct(self, codegen):
raise NotImplementedError()
raise NotImplementedError
def can_reconstruct(self, tx):
"""If it is possible to reconstruct the Python object this
@ -273,7 +273,7 @@ class VariableTracker(metaclass=VariableTrackerMeta):
return False
def unpack_var_sequence(self, tx) -> List["VariableTracker"]:
raise NotImplementedError()
raise NotImplementedError
def has_unpack_var_sequence(self, tx) -> bool:
try:

4
torch/_dynamo/variables/constant.py
View File

@ -122,7 +122,7 @@ class ConstantVariable(VariableTracker):
)
member = getattr(self.value, name)
if callable(member):
raise NotImplementedError()
raise NotImplementedError
return member
def call_method(
@ -212,5 +212,5 @@ class EnumVariable(VariableTracker):
def const_getattr(self, tx, name):
member = getattr(self.value, name)
if callable(member):
raise NotImplementedError()
raise NotImplementedError
return member

8
torch/_dynamo/variables/dicts.py
View File

@ -420,7 +420,7 @@ class DictView(VariableTracker):
def view_items_vt(self):
# Returns an iterable of the unpacked items
# Implement in the subclasses
raise NotImplementedError()
raise NotImplementedError
def unpack_var_sequence(self, tx):
def unwrap(x):
@ -615,7 +615,7 @@ class DataClassVariable(ConstDictVariable):
assert self.is_matching_cls(user_cls)
def as_proxy(self):
raise NotImplementedError()
raise NotImplementedError
def reconstruct(self, codegen):
codegen.extend_output([codegen._create_load_const(self.user_cls)])
@ -737,14 +737,14 @@ class CustomizedDictVariable(ConstDictVariable):
# called from builder.py
@classmethod
def wrap(cls, builder, obj):
raise NotImplementedError()
raise NotImplementedError
def __init__(self, items, user_cls, **options):
super().__init__(items, user_cls, **options)
assert self.is_matching_cls(user_cls)
def as_proxy(self):
raise NotImplementedError()
raise NotImplementedError
# 'RETURN_VALUE triggered compile'
# called from torch/_dynamo/codegen.py

2
torch/_dynamo/variables/functions.py
View File

@ -439,7 +439,7 @@ class NestedUserFunctionVariable(BaseUserFunctionVariable):
def get_function(self):
if self.closure:
raise NotImplementedError()
raise NotImplementedError
func = types.FunctionType(
self.code.as_python_constant(),
self.f_globals,

10
torch/_dynamo/variables/lists.py
View File

@ -160,7 +160,7 @@ class RangeVariable(BaseListVariable):
elif len(items_to_map) == 3:
start, stop, step = items_to_map
else:
raise AssertionError()
raise AssertionError
assert stop is not None
super().__init__([start, stop, step], **kwargs)
@ -592,7 +592,7 @@ class SliceVariable(BaseListVariable):
elif len(items_to_map) == 3:
start, stop, step = items_to_map
else:
raise AssertionError()
raise AssertionError
if isinstance(start, variables.TensorVariable) or isinstance(
stop, variables.TensorVariable
@ -644,7 +644,7 @@ class ListIteratorVariable(VariableTracker):
assert self.mutable_local
old_index = self.index
if old_index >= len(self.items):
raise StopIteration()
raise StopIteration
tx.output.side_effects.mutation(self)
self.index += 1
return self.items[old_index]
@ -665,7 +665,7 @@ class ListIteratorVariable(VariableTracker):
def as_python_constant(self):
if self.index > 0:
raise NotImplementedError()
raise NotImplementedError
return iter([x.as_python_constant() for x in self.items])
def unpack_var_sequence(self, tx):
@ -748,7 +748,7 @@ class RestrictedListSubclassVariable(ListVariable):
return [x.as_proxy() for x in self.items]
def as_python_constant(self):
raise NotImplementedError()
raise NotImplementedError
def is_python_constant(self):
return False

6
torch/_dynamo/variables/misc.py
View File

@ -592,13 +592,13 @@ class GetAttrVariable(VariableTracker):
def const_getattr(self, tx, name):
if not isinstance(self.obj, variables.NNModuleVariable):
raise NotImplementedError()
raise NotImplementedError
step1 = tx.output.get_submodule(self.obj.module_key)
if self.name not in step1.__dict__:
raise NotImplementedError()
raise NotImplementedError
step2 = inspect.getattr_static(step1, self.name)
if name not in step2.__dict__:
raise NotImplementedError()
raise NotImplementedError
return inspect.getattr_static(step2, name)
def reconstruct(self, codegen):

2
torch/_dynamo/variables/nn_module.py
View File

@ -153,7 +153,7 @@ class NNModuleVariable(VariableTracker):
# Mark the class dynamic unless its module initialization
if tx.f_code.co_name != "__init__":
GenerationTracker.mark_class_dynamic(type(mod))
raise UnspecializeRestartAnalysis()
raise UnspecializeRestartAnalysis
def _custom_getattr_fallback(self, base, tx, name, options):
"""Check for a __getattr__ and handle it specially if it is implemented"""

2
torch/_dynamo/variables/optimizer.py
View File

@ -157,7 +157,7 @@ class OptimizerVariable(UserDefinedObjectVariable):
):
return self.value.param_groups[arg.source.index]
raise ArgMappingException()
raise ArgMappingException
new_args = [map_arg(arg) for arg in args]
new_kwargs = {k: map_arg(v) for k, v in kwargs.items()}

16
torch/_dynamo/variables/tensor.py
View File

@ -222,7 +222,7 @@ class TensorVariable(VariableTracker):
return SourcelessBuilder.create(tx, example_value)
if not (self.source and self.source.subguards_allowed()):
raise NotImplementedError()
raise NotImplementedError
# For local source, we associate the real value. We use this real value
# for implementing getattr fallthrough on the variable tracker base class.
@ -238,23 +238,23 @@ class TensorVariable(VariableTracker):
# Which is incorrect, and violates the invariant that all sources should be eval()-able against the scope.
_input_associated_real_value = eval(self.source.name(), scope)
except Exception as exc:
raise NotImplementedError() from exc
raise NotImplementedError from exc
if _input_associated_real_value is None:
raise NotImplementedError()
raise NotImplementedError
if object_has_getattribute(_input_associated_real_value):
raise NotImplementedError()
raise NotImplementedError
if get_custom_getattr(_input_associated_real_value):
raise NotImplementedError()
raise NotImplementedError
real_value = getattr(_input_associated_real_value, name)
if callable(real_value):
# Callables have more nuanced handling, and we should let the existing system delegate here.
# Raising was past behavior and so should always be sound to fall back.
# Note - at a certain point we may want to handle
raise NotImplementedError()
raise NotImplementedError
from ..guards import GuardBuilder
from .builder import VariableBuilder
@ -391,7 +391,7 @@ class TensorVariable(VariableTracker):
result = self.dynamic_getattr(tx, name)
if result is None:
raise NotImplementedError()
raise NotImplementedError
return result
def has_unpack_var_sequence(self, tx):
@ -1090,7 +1090,7 @@ class NumpyNdarrayVariable(TensorVariable):
elif name in ["__version__"]:
unimplemented("delegate np.__version__ to NumPy")
if result is None:
raise NotImplementedError()
raise NotImplementedError
return result
@staticmethod

2
torch/_functorch/pyfunctorch.py
View File

@ -72,7 +72,7 @@ class FuncTorchInterpreter(ABC):
return self._cptr.key()
def get_state(self):
raise NotImplementedError()
raise NotImplementedError
def check_state(self, state):
return state == self.get_state()

8
torch/_guards.py
View File

@ -797,17 +797,17 @@ class Source:
return False
def reconstruct(self, codegen):
raise NotImplementedError()
raise NotImplementedError
def guard_source(self) -> GuardSource:
raise NotImplementedError()
raise NotImplementedError
def name(self) -> str:
raise NotImplementedError()
raise NotImplementedError
def make_guard(self, fn) -> Guard:
if self.guard_source() is GuardSource.CONSTANT:
raise NotImplementedError()
raise NotImplementedError
return Guard(self, fn)
def is_nn_module(self) -> bool:

2
torch/_inductor/autotune_process.py
View File

@ -422,7 +422,7 @@ class BenchmarkRequest:
def make_run_fn(
self, *input_tensors: torch.Tensor, output_tensor: torch.Tensor
) -> Callable[[], None]:
raise NotImplementedError()
raise NotImplementedError
def cleanup_run_fn(self) -> None:
pass

14
torch/_inductor/codecache.py
View File

@ -446,7 +446,7 @@ def _reduce_tensor(t):
# TODO: These tensors don't currently pickle, so we can't cache a
# compiled graph containing them. Just fail now. If mkldnn tensors
# get pickling support, we can remove this.
raise BypassFxGraphCache()
raise BypassFxGraphCache
# Very large tensors could be expensive to copy to cpu and hash. Let's
# at least report if we find slowness.
@ -598,7 +598,7 @@ class FxGraphHashDetails:
# Some configs options are callables, e.g., post_grad_custom_pre_pass,
# and may not pickle.
log.debug("Can't pickle inductor config: %s", e)
raise BypassFxGraphCache() from e
raise BypassFxGraphCache from e
def debug_str(self) -> str:
"""
@ -843,19 +843,19 @@ class FxGraphCache:
"""
# Freezing can embed constants that wouldn't be static across runs.
if config.freezing or config.aot_inductor.use_runtime_constant_folding:
raise BypassFxGraphCache()
raise BypassFxGraphCache
# The treatment of guards in the caching implementation requires that
# we have a shape env.
if FxGraphCache._get_shape_env() is None:
log.debug("fx graph cache no shape env")
raise BypassFxGraphCache()
raise BypassFxGraphCache
# HigherOrderOperators should be handled on a case-by-case basis.
# Currently, we just skip caching if we have any.
for node in gm.graph.nodes:
if isinstance(node.target, torch._ops.HigherOrderOperator):
raise BypassFxGraphCache()
raise BypassFxGraphCache
@staticmethod
def load(
@ -990,7 +990,7 @@ def cpp_compiler_search(search: str) -> str:
return cxx
except (subprocess.SubprocessError, FileNotFoundError, ImportError):
continue
raise exc.InvalidCxxCompiler()
raise exc.InvalidCxxCompiler
def install_gcc_via_conda() -> str:
@ -2745,7 +2745,7 @@ def _worker_compile_triton(
class CodeCacheFuture:
def result(self):
raise NotImplementedError()
raise NotImplementedError
class TritonFuture(CodeCacheFuture):

26
torch/_inductor/codegen/common.py
View File

@ -88,16 +88,16 @@ device_codegens: Dict[str, DeviceCodegen] = {}
class DeviceOpOverrides:
def import_get_raw_stream_as(self, name):
raise NotImplementedError()
raise NotImplementedError
def set_device(self, device_idx):
raise NotImplementedError()
raise NotImplementedError
def synchronize(self):
raise NotImplementedError()
raise NotImplementedError
def device_guard(self, device_idx):
raise NotImplementedError()
raise NotImplementedError
device_op_overrides_dict: Dict[str, DeviceOpOverrides] = {}
@ -1368,7 +1368,7 @@ class Kernel(CodeGen):
self.cse = cse
def load(self, name: str, index: sympy.Expr) -> CSEVariable:
raise NotImplementedError()
raise NotImplementedError
def indirect_load(self, name: str, index: sympy.Expr):
"""A load the depends on an index we have read"""
@ -1381,12 +1381,12 @@ class Kernel(CodeGen):
self.loads = prior
def store_reduction(self, name: str, index: sympy.Expr, value: CSEVariable):
raise NotImplementedError()
raise NotImplementedError
def store(
self, name: str, index: sympy.Expr, value: CSEVariable, mode: StoreMode = None
) -> None:
raise NotImplementedError()
raise NotImplementedError
def reduction(
self,
@ -1395,7 +1395,7 @@ class Kernel(CodeGen):
reduction_type: ReductionType,
value: Union[CSEVariable, Tuple[CSEVariable, ...]],
) -> Union[CSEVariable, Tuple[CSEVariable, ...]]:
raise NotImplementedError()
raise NotImplementedError
def scan(
self,
@ -1405,7 +1405,7 @@ class Kernel(CodeGen):
],
values: Tuple[CSEVariable, ...],
) -> Tuple[CSEVariable, ...]:
raise NotImplementedError()
raise NotImplementedError
def bucketize(
self,
@ -1418,11 +1418,11 @@ class Kernel(CodeGen):
"""
See [Note: Inductor bucketize op]
"""
raise NotImplementedError()
raise NotImplementedError
@property
def assert_function(self) -> str:
raise NotImplementedError()
raise NotImplementedError
def indirect_assert(self, var, lower, upper, mask=None):
if lower and upper:
@ -1444,7 +1444,7 @@ class Kernel(CodeGen):
return f'{self.assert_function}({cond}, "index out of bounds: {cond_print}")'
def index_to_str(self, index: sympy.Expr) -> str:
raise NotImplementedError()
raise NotImplementedError
def __enter__(self):
# TODO: hoist this to top level
@ -1737,4 +1737,4 @@ class KernelTemplate:
Generates a ChoiceCaller instance from the given arguments.
"""
raise NotImplementedError()
raise NotImplementedError

2
torch/_inductor/codegen/cpp.py
View File

@ -2659,7 +2659,7 @@ class CppVecKernel(CppKernel):
mean, m2, weight = reduction_project(reduction_type, next_value)
return f"welford_combine({var}, {{{mean}, {m2}, {weight}}})"
else:
raise NotImplementedError()
raise NotImplementedError
def indirect_assert(self, var, lower, upper, mask=None):
assert not mask, "do not support mask in indirect_indexing assertion"

4
torch/_inductor/codegen/cuda/cutlass_epilogue_gen.py
View File

@ -197,7 +197,7 @@ class CutlassEVTEpilogueTypeFormatter:
return f"cutlass::epilogue::fusion::Sm90EVT<cutlass::epilogue::fusion::Sm90Compute<cutlass::maximum, ElementAcc, ElementAcc, RoundStyle>,{a}, {const_zero}>" # noqa: B950
def reduction(self, dtype, src_dtype, reduction_type, value):
raise CUTLASSEVTOpNotImplementedError()
raise CUTLASSEVTOpNotImplementedError
# Add more ops here...
def getvalue(self, result) -> str:
@ -354,7 +354,7 @@ class CutlassEVTEpilogueArgumentFormatter:
return a
def reduction(self, dtype, src_dtype, reduction_type, value):
raise CUTLASSEVTOpNotImplementedError()
raise CUTLASSEVTOpNotImplementedError
def getvalue(self, result) -> str:
return "{" + str(result) + "}"

6
torch/_inductor/codegen/memory_planning.py
View File

@ -134,15 +134,15 @@ class AllocationTreeNode:
def get_live_ranges(self) -> LiveRanges:
"""Aggregate LiveRanges for all objects below this in tree"""
raise NotImplementedError()
raise NotImplementedError
def get_size_hint(self) -> int:
"""Number of bytes used for example inputs"""
raise NotImplementedError()
raise NotImplementedError
def get_symbolic_size(self) -> sympy.Expr:
"""Number of bytes needed at runtime"""
raise NotImplementedError()
raise NotImplementedError
def finalize(self, pool, offset) -> AllocationTreeNode:
"""Called after all allocations have been made"""

4
torch/_inductor/codegen/triton.py
View File

@ -1468,7 +1468,7 @@ class TritonKernel(Kernel):
def add_range(i, expr):
expr = sv.simplify(expr)
if not sv.statically_known_multiple_of(remaining[i], expr):
raise CantSplit()
raise CantSplit
# guard on the last item out
remaining[i] = FloorDiv(remaining[i], expr)
new_ranges[i].append(expr)
@ -1501,7 +1501,7 @@ class TritonKernel(Kernel):
if not sv.statically_known_multiple_of(
size, remaining[current_group]
):
raise CantSplit()
raise CantSplit
size1 = remaining[current_group]
size2 = FloorDiv(size, remaining[current_group])
return_getters.append(

2
torch/_inductor/codegen/wrapper.py
View File

@ -1346,7 +1346,7 @@ class WrapperCodeGen(CodeGen):
self.lines.append(LineContext(ctx))
def val_to_cpp_arg_str(self, type_, val) -> str:
raise NotImplementedError()
raise NotImplementedError
def val_to_arg_str(self, s):
from torch.utils._triton import dtype_to_string, has_triton_package

4
torch/_inductor/graph.py
View File

@ -954,10 +954,10 @@ class GraphLowering(torch.fx.Interpreter):
return self.add_tensor_constant(value, target)
def call_module(self, target, args, kwargs):
raise AssertionError()
raise AssertionError
def call_method(self, target, args, kwargs):
raise AssertionError()
raise AssertionError
def output(self, target, args, kwargs):
result = super().output(target, args, kwargs)

18
torch/_inductor/ir.py
View File

@ -2252,7 +2252,7 @@ class View(GenericView):
size_old = size_old * modulus
V.graph.sizevars.guard_equals(size_new, size_old)
else:
raise AssertionError()
raise AssertionError
while stack_old:
size_old = stack_old.pop()
@ -2818,7 +2818,7 @@ class FlexibleLayout(Layout):
"stride_ordered_for_memory_format, unsuppored memory_format: %s",
memory_format,
)
raise NotImplementedError()
raise NotImplementedError
@staticmethod
def same_ordered(sizes, stride):
@ -3666,16 +3666,16 @@ class ChoiceCaller:
return do_bench(lambda: algo(*args, out=out))
def call_name(self) -> str:
raise NotImplementedError()
raise NotImplementedError
def to_callable(self):
raise NotImplementedError()
raise NotImplementedError
def hash_key(self) -> str:
raise NotImplementedError()
raise NotImplementedError
def output_node(self) -> "TensorBox":
raise NotImplementedError()
raise NotImplementedError
def info_dict(self) -> Dict[str, Union[PrimitiveInfoType, List[PrimitiveInfoType]]]:
"""Information returned here is logged to the autotune log file when that is enabled."""
@ -3684,7 +3684,7 @@ class ChoiceCaller:
class TritonTemplateCallerBase(ChoiceCaller):
def get_make_kernel_render(self) -> Any:
raise NotImplementedError()
raise NotImplementedError
class MultiTemplateBuffer(TritonTemplateBuffer):
@ -4033,7 +4033,7 @@ class ExternKernel(InputsKernel):
wrapper.writeline(origin_str)
def codegen(self, wrapper):
raise NotImplementedError()
raise NotImplementedError
def get_kernel_name(self):
return self.cpp_kernel_name if V.graph.cpp_wrapper else self.python_kernel_name
@ -4157,7 +4157,7 @@ class ExternKernel(InputsKernel):
offset,
index,
)
raise NotImplementedError()
raise NotImplementedError
return ReinterpretView(
data=x.data,

2
torch/_inductor/lowering.py
View File

@ -1629,7 +1629,7 @@ def bernoulli_p(x, *args):
# This shouldn't be called in general
@register_lowering(aten._foobar)
def _foobar(_):
raise AssertionError()
raise AssertionError
@functools.lru_cache(1)

8
torch/_inductor/pattern_matcher.py
View File

@ -218,7 +218,7 @@ class PatternExpr:
def _match(
self, node: torch.fx.Node, ctx: MatchContext
) -> Union[Match, FailedMatch]:
raise NotImplementedError()
raise NotImplementedError
def match(self, node: torch.fx.Node) -> Union[Match, FailedMatch]:
try:
@ -361,7 +361,7 @@ class _TargetExpr(PatternExpr):
return isinstance(self.users, Multiple) or self.users > 1
def find_anchor_nodes(self, ctx: MatchContext, searched):
raise NotImplementedError()
raise NotImplementedError
def _match_fns(self, node: torch.fx.Node):
return (
@ -803,7 +803,7 @@ class PatternEntry:
extra_check: Callable[[Match], bool]
def apply(self, match: Match, graph: torch.fx.Graph, node: torch.fx.Node):
raise NotImplementedError()
raise NotImplementedError
def register(self, pass_dicts, target=None, prepend=False):
if target is None:
@ -1507,7 +1507,7 @@ class PatternMatcherPass:
def _not_implemented(*args, **kwargs) -> NoReturn:
raise NotImplementedError()
raise NotImplementedError
def fx_to_pattern(

16
torch/_inductor/scheduler.py
View File

@ -2514,13 +2514,13 @@ class BaseScheduling:
"""
Check whether node1 and node2 can be vertically fused or not.
"""
raise NotImplementedError()
raise NotImplementedError
def can_fuse_horizontal(self, node1: BaseSchedulerNode, node2: BaseSchedulerNode):
"""
Check whether node1 and node2 can be horizontally fused or not.
"""
raise NotImplementedError()
raise NotImplementedError
def fuse(self, node1: BaseSchedulerNode, node2: BaseSchedulerNode):
"""
@ -2535,7 +2535,7 @@ class BaseScheduling:
"""
Process the iteration sizes in case a transformation needs to be applied.
"""
raise NotImplementedError()
raise NotImplementedError
def codegen_template(
self, template_node: SchedulerNode, epilogue_nodes: List[SchedulerNode]
@ -2546,19 +2546,19 @@ class BaseScheduling:
This function is only available for triton now. If the third-party backend behaves as a sub-class
of TritonScheduling, it can override it or reuse it.
"""
raise NotImplementedError()
raise NotImplementedError
def codegen_node(self, node: Union[FusedSchedulerNode, SchedulerNode]):
"""
Generate a kernel given a list of pre-fused nodes.
"""
raise NotImplementedError()
raise NotImplementedError
def codegen_sync(self):
"""
Generate synchronization code for the kernel. This method depends on the hardware characteristics.
"""
raise NotImplementedError()
raise NotImplementedError
def ready_to_flush(self) -> bool:
"""
@ -2571,14 +2571,14 @@ class BaseScheduling:
"""
Flush the generated kernel and python wrapper code to the source code file.
"""
raise NotImplementedError()
raise NotImplementedError
def benchmark_fused_nodes(self, nodes):
"""
Benchmark fused list of nodes and return the execution time
in milliseconds on randomly generated inputs.
"""
raise NotImplementedError()
raise NotImplementedError
def get_fusion_pair_priority(self, node1, node2) -> int:
"""

4
torch/_inductor/utils.py
View File

@ -949,11 +949,11 @@ class DeferredLineBase:
def __call__(self) -> Optional[str]:
"""Returns either self.line or None to indicate the line has been 'unwritten'"""
raise NotImplementedError()
raise NotImplementedError
def _new_line(self, line: str) -> DeferredLineBase:
"""Returns a new deferred line with the same condition"""
raise NotImplementedError()
raise NotImplementedError
def with_prefix(self, prefix):
return self._new_line(f"{prefix}{self.line}")

2
torch/_numpy/_funcs_impl.py
View File

@ -217,7 +217,7 @@ def _split_helper_int(tensor, indices_or_sections, axis, strict=False):
l, n = tensor.shape[axis], indices_or_sections
if n <= 0:
raise ValueError()
raise ValueError
if l % n == 0:
num, sz = n, l // n

2
torch/_numpy/_ndarray.py
View File

@ -512,7 +512,7 @@ def array(obj, dtype=None, *, copy=True, order="K", subok=False, ndmin=0, like=N
if like is not None:
raise NotImplementedError("'like' parameter is not supported.")
if order != "K":
raise NotImplementedError()
raise NotImplementedError
# a happy path
if (

2
torch/_numpy/_normalizations.py
View File

@ -174,7 +174,7 @@ def maybe_copy_to(out, result, promote_scalar_result=False):
maybe_copy_to(o, r, promote_scalar_result) for o, r in zip(out, result)
)
else:
raise AssertionError() # We should never hit this path
raise AssertionError # We should never hit this path
def wrap_tensors(result):

4
torch/_ops.py
View File

@ -85,7 +85,7 @@ class OperatorBase:
self.functorch_table = {}
def __call__(self, *args, **kwargs):
raise NotImplementedError()
raise NotImplementedError
def has_kernel_for_dispatch_key(self, k):
return k in self.py_kernels
@ -165,7 +165,7 @@ class OperatorBase:
return fn
def name(self):
raise NotImplementedError()
raise NotImplementedError
is_included_in_alias = torch._C._dispatch_is_included_in_alias

18
torch/_streambase.py
View File

@ -6,27 +6,27 @@ class _StreamBase(ABC):
@abstractmethod
def wait_event(self, event):
raise NotImplementedError()
raise NotImplementedError
@abstractmethod
def wait_stream(self, stream):
raise NotImplementedError()
raise NotImplementedError
@abstractmethod
def record_event(self, event=None):
raise NotImplementedError()
raise NotImplementedError
@abstractmethod
def query(self):
raise NotImplementedError()
raise NotImplementedError
@abstractmethod
def synchronize(self):
raise NotImplementedError()
raise NotImplementedError
@abstractmethod
def __eq__(self, stream):
raise NotImplementedError()
raise NotImplementedError
class _EventBase(ABC):
@ -34,12 +34,12 @@ class _EventBase(ABC):
@abstractmethod
def wait(self, stream=None):
raise NotImplementedError()
raise NotImplementedError
@abstractmethod
def query(self):
raise NotImplementedError()
raise NotImplementedError
@abstractmethod
def synchronize(self):
raise NotImplementedError()
raise NotImplementedError

2
torch/cuda/_memory_viz.py
View File

@ -354,7 +354,7 @@ def trace(data):
elif e['action'] == 'oom':
size = e['size']
free = e['device_free']
out.write(f'raise OutOfMemoryError() # {Bytes(size)} requested, {Bytes(free)} free in CUDA\n')
out.write(f'raise OutOfMemoryError # {Bytes(size)} requested, {Bytes(free)} free in CUDA\n')
else:
out.write(f'{e}\n')
out.write(f"TOTAL MEM: {Bytes(count)}")

2
torch/distributed/_composable/fsdp/_fsdp_param.py
View File

@ -465,7 +465,7 @@ class FSDPParam:
return [_to_dtype_if_needed(sharded_param_data, self.param_dtype)]
elif self.sharded_state == ShardedState.SHARDED_POST_FORWARD:
if hasattr(self._sharded_local_tensor, "fsdp_pre_all_gather"):
raise NotImplementedError()
raise NotImplementedError
all_gather_input = _to_dtype_if_needed(
cast(torch.Tensor, self._sharded_post_forward_param_data),
self.param_dtype,

4
torch/distributed/_spmd/parallel_mode.py
View File

@ -39,7 +39,7 @@ class ParallelMode(ABC):
TODO(@wanchaol): some of these arguments are not necessary for
partitioning, remove the unnecessary ones later.
"""
raise NotImplementedError()
raise NotImplementedError
@abstractmethod
def transform_and_compile(self, gm: GraphModule) -> GraphModule:
@ -51,7 +51,7 @@ class ParallelMode(ABC):
the distributed environment.
"""
# TODO: add more necessary arguments to this interface.
raise NotImplementedError()
raise NotImplementedError
class DataParallel(ParallelMode):

4
torch/distributed/_state_dict_utils.py
View File

@ -146,7 +146,7 @@ def _iterate_state_dict(
not isinstance(companion_obj, (list, tuple))
or len(companion_obj) != len(iter_object)
):
raise CompanionMismatch()
raise CompanionMismatch
ret = [
_iterate_state_dict(
@ -437,7 +437,7 @@ def _check_state_dict_similarity(
companion_obj: Any,
) -> torch.Tensor:
if companion_obj.dtype != obj.dtype or companion_obj.size() != obj.size():
raise CompanionMismatch()
raise CompanionMismatch
return obj
try:

12
torch/distributed/elastic/agent/server/api.py
View File

@ -439,7 +439,7 @@ class ElasticAgent(abc.ABC):
Raises:
Exception - any other failures NOT related to worker process
"""
raise NotImplementedError()
raise NotImplementedError
@abc.abstractmethod
def get_worker_group(self, role: str = DEFAULT_ROLE) -> WorkerGroup:
@ -450,7 +450,7 @@ class ElasticAgent(abc.ABC):
Implementors are encouraged (but not required) to return
a defensive read-only copy.
"""
raise NotImplementedError()
raise NotImplementedError
class SimpleElasticAgent(ElasticAgent):
@ -477,7 +477,7 @@ class SimpleElasticAgent(ElasticAgent):
This is according to worker spec for the worker group .
Returns a map of ``local_rank`` to worker ``id``.
"""
raise NotImplementedError()
raise NotImplementedError
@abc.abstractmethod
def _stop_workers(self, worker_group: WorkerGroup) -> None:
@ -487,7 +487,7 @@ class SimpleElasticAgent(ElasticAgent):
``WorkerState``. That is, it must gracefully handle stopping
non-existent workers, unhealthy (stuck) workers, etc.
"""
raise NotImplementedError()
raise NotImplementedError
@abc.abstractmethod
def _monitor_workers(self, worker_group: WorkerGroup) -> RunResult:
@ -495,7 +495,7 @@ class SimpleElasticAgent(ElasticAgent):
This function also returns the new state of the worker group.
"""
raise NotImplementedError()
raise NotImplementedError
@abc.abstractmethod
def _shutdown(self, death_sig: signal.Signals = signal.SIGTERM) -> None:
@ -504,7 +504,7 @@ class SimpleElasticAgent(ElasticAgent):
Args:
death_sig: Signal to send to the child process, SIGTERM is default
"""
raise NotImplementedError()
raise NotImplementedError
@staticmethod
def _set_master_addr_port(

8
torch/distributed/elastic/multiprocessing/api.py
View File

@ -458,7 +458,7 @@ class PContext(abc.ABC):
@abc.abstractmethod
def _start(self) -> None:
"""Start processes using strategy defined in a particular context."""
raise NotImplementedError()
raise NotImplementedError
@abc.abstractmethod
def _poll(self) -> Optional[RunProcsResult]:
@ -469,7 +469,7 @@ class PContext(abc.ABC):
successfully or any process fails. Returns ``None`` if
all processes are still running.
"""
raise NotImplementedError()
raise NotImplementedError
def wait(self, timeout: float = -1, period: float = 1) -> Optional[RunProcsResult]:
"""
@ -514,7 +514,7 @@ class PContext(abc.ABC):
@abc.abstractmethod
def pids(self) -> Dict[int, int]:
"""Return pids of processes mapped by their respective local_ranks."""
raise NotImplementedError()
raise NotImplementedError
@abc.abstractmethod
def _close(self, death_sig: signal.Signals, timeout: int = 30) -> None:
@ -522,7 +522,7 @@ class PContext(abc.ABC):
Terminates all processes managed by this context and cleans up any
meta resources (e.g. redirect, error_file files).
"""
raise NotImplementedError()
raise NotImplementedError
def close(
self, death_sig: Optional[signal.Signals] = None, timeout: int = 30

4
torch/distributed/elastic/rendezvous/dynamic_rendezvous.py
View File

@ -655,10 +655,10 @@ class _DistributedRendezvousOpExecutor(_RendezvousOpExecutor):
continue
if action == _Action.ERROR_CLOSED:
raise RendezvousClosedError()
raise RendezvousClosedError
if action == _Action.ERROR_TIMEOUT:
raise RendezvousTimeoutError()
raise RendezvousTimeoutError
if action == _Action.SYNC:
# Delay the execution by one second to avoid overloading the

14
torch/distributed/elastic/rendezvous/etcd_rendezvous.py
View File

@ -270,7 +270,7 @@ class EtcdRendezvous:
self._rendezvous_deadline = time.time() + self._timeout
while True:
if time.time() > self._rendezvous_deadline:
raise RendezvousTimeoutError()
raise RendezvousTimeoutError
logger.info("Attempting to join next rendezvous")
try:
@ -340,17 +340,17 @@ class EtcdRendezvous:
logger.info("Observed existing rendezvous state: %s", state)
if state["status"] == "closed":
raise RendezvousClosedError()
raise RendezvousClosedError
if state["status"] == "joinable":
return self.join_phase(state["version"])
if state["status"] == "final":
self.handle_existing_rendezvous(state["version"])
raise EtcdRendezvousRetryImmediately()
raise EtcdRendezvousRetryImmediately
self.try_wait_for_state_change(etcd_index=active_version.etcd_index + 1)
raise EtcdRendezvousRetryableFailure()
raise EtcdRendezvousRetryableFailure
def join_phase(self, expected_version):
"""
@ -632,7 +632,7 @@ class EtcdRendezvous:
active_version, state = self.get_rdzv_state()
if state["status"] != "final" or state["version"] != expected_version:
raise EtcdRendezvousRetryImmediately()
raise EtcdRendezvousRetryImmediately
# Increment counter to signal an additional waiting worker.
state["num_workers_waiting"] += 1
@ -714,7 +714,7 @@ class EtcdRendezvous:
pass
if time.time() > self._rendezvous_deadline:
raise RendezvousTimeoutError()
raise RendezvousTimeoutError
active_version, state = self.get_rdzv_state()
def handle_join_last_call(self, expected_version, deadline):
@ -832,7 +832,7 @@ class EtcdRendezvous:
pass
if time.time() > self._rendezvous_deadline:
raise RendezvousTimeoutError()
raise RendezvousTimeoutError
# Unfortunately, we have to do another fetch in order to get last etcd_index.
return self.get_rdzv_state()

2
torch/fx/_symbolic_trace.py
View File

@ -963,7 +963,7 @@ class _PatchedFn(NamedTuple):
orig_fn: Any
def revert(self):
raise NotImplementedError()
raise NotImplementedError
class _PatchedFnSetItem(_PatchedFn):

2
torch/fx/experimental/_sym_dispatch_mode.py
View File

@ -22,7 +22,7 @@ SYM_FUNCTION_MODE: Optional["SymDispatchMode"] = None
#
class SymDispatchMode:
def __sym_dispatch__(self, func, types, args, kwargs):
raise NotImplementedError()
raise NotImplementedError
def __enter__(self):
global SYM_FUNCTION_MODE

2
torch/fx/passes/operator_support.py
View File

@ -31,7 +31,7 @@ class OperatorSupportBase(abc.ABC):
def is_node_supported(
self, submodules: t.Mapping[str, torch.nn.Module], node: torch.fx.Node
) -> bool:
raise NotImplementedError()
raise NotImplementedError
@compatibility(is_backward_compatible=False)

4
torch/jit/_script.py
View File

@ -476,7 +476,7 @@ if _enabled:
# RecursiveScriptClass.
def forward_magic_method(self, method_name, *args, **kwargs):
if not self._c._has_method(method_name):
raise TypeError()
raise TypeError
self_method = self.__getattr__(method_name)
return self_method(*args, **kwargs)
@ -865,7 +865,7 @@ if _enabled:
if getattr(self_method, "__func__", None) == getattr(
RecursiveScriptModule, method_name
):
raise NotImplementedError()
raise NotImplementedError
return self_method(*args, **kwargs)
def __iter__(self):

2
torch/nn/modules/conv.py
View File

@ -1184,7 +1184,7 @@ class _LazyConvXdMixin(LazyModuleMixin):
# Function to return the number of spatial dims expected for inputs to the module.
# This is expected to be implemented by subclasses.
def _get_num_spatial_dims(self) -> int:
raise NotImplementedError()
raise NotImplementedError
# LazyConv1d defines weight as a Tensor but derived class defines it as UnitializeParameter

2
torch/onnx/_internal/diagnostics/infra/_infra.py
View File

@ -264,7 +264,7 @@ class Invocation:
# TODO: Implement this.
# Tracks top level call arguments and diagnostic options.
def __init__(self) -> None:
raise NotImplementedError()
raise NotImplementedError
@dataclasses.dataclass

2
torch/overrides.py
View File

@ -1910,7 +1910,7 @@ class TorchFunctionMode:
pass
def __torch_function__(self, func, types, args=(), kwargs=None):
raise NotImplementedError()
raise NotImplementedError
def __enter__(self):
_push_mode(self)

6
torch/testing/_comparison.py
View File

@ -36,7 +36,7 @@ class ErrorMeta(Exception):
super().__init__(
"If you are a user and see this message during normal operation "
"please file an issue at https://github.com/pytorch/pytorch/issues. "
"If you are a developer and working on the comparison functions, please `raise ErrorMeta().to_error()` "
"If you are a developer and working on the comparison functions, please `raise ErrorMeta.to_error()` "
"for user facing errors."
)
self.type = type
@ -336,7 +336,7 @@ class Pair(abc.ABC):
@staticmethod
def _inputs_not_supported() -> NoReturn:
raise UnsupportedInputs()
raise UnsupportedInputs
@staticmethod
def _check_inputs_isinstance(*inputs: Any, cls: Union[Type, Tuple[Type, ...]]):
@ -1217,7 +1217,7 @@ def not_close_error_metas(
)
except ErrorMeta as error_meta:
# Explicitly raising from None to hide the internal traceback
raise error_meta.to_error() from None
raise error_meta.to_error() from None # noqa: RSE102
error_metas: List[ErrorMeta] = []
for pair in pairs:

4
torch/testing/_internal/distributed/distributed_test.py
View File

@ -9390,7 +9390,7 @@ class DistributedTest:
@staticmethod
def backward(ctx, grad_output):
raise RuntimeError()
raise RuntimeError
class MyModel(nn.Module):
def __init__(self, device):
@ -9534,7 +9534,7 @@ class DistributedTest:
@staticmethod
def backward(ctx, grad_output):
raise RuntimeError()
raise RuntimeError
class MyModel(torch.nn.Module):
def __init__(self, device):

2
torch/utils/_python_dispatch.py
View File

@ -67,7 +67,7 @@ class TorchDispatchMode:
self.old_dispatch_mode_flag = False
def __torch_dispatch__(self, func, types, args=(), kwargs=None):
raise NotImplementedError()
raise NotImplementedError
def __enter__(self):
global _is_in_torch_dispatch_mode

2
torch/utils/checkpoint.py
View File

@ -1085,7 +1085,7 @@ class _recomputation_hook(torch.autograd.graph.saved_tensors_hooks):
if target_frame.early_stop and target_frame.recomp_counter[gid] == len(
target_frame.weak_holders
):
raise _StopRecomputationError()
raise _StopRecomputationError
# See Rule 6: [ retain_graph is True ] above
return x.detach()

2
torch/utils/data/_utils/fetch.py
View File

@ -12,7 +12,7 @@ class _BaseDatasetFetcher:
self.drop_last = drop_last
def fetch(self, possibly_batched_index):
raise NotImplementedError()
raise NotImplementedError
class _IterableDatasetFetcher(_BaseDatasetFetcher):

2
torch/utils/data/datapipes/dataframe/dataframes.py
View File

@ -92,7 +92,7 @@ class Capture:
if attrname == 'kwarg' or attrname == 'kwargs':
raise Exception('no kwargs!')
if attrname in ['__deepcopy__']:
raise AttributeError()
raise AttributeError
result = CaptureGetAttr(self, attrname, ctx=self.ctx)
return result

2
torch/utils/data/sampler.py
View File

@ -79,7 +79,7 @@ class Sampler(Generic[T_co]):
# Calling `len(subclass_instance)` raises:
# TypeError: 'NotImplementedType' object cannot be interpreted as an integer
#
# + `raise NotImplementedError()`:
# + `raise NotImplementedError`:
# This prevents triggering some fallback behavior. E.g., the built-in
# `list(X)` tries to call `len(X)` first, and executes a different code
# path if the method is not found or `NotImplemented` is returned, while

2
torchgen/dest/ufunc.py
View File

@ -507,7 +507,7 @@ def compute_ufunc_cpu_kernel(g: NativeFunctionsGroup) -> str:
elif k is UfuncKey.CPUVector:
compute_t = VectorizedCType(BaseCType(scalar_t))
else:
raise AssertionError()
raise AssertionError
inner_ufunc_sigs = ufunc_sigs.setdefault(dtype, {})
if k not in inner_ufunc_sigs:
inner_ufunc_sigs[k] = UfuncSignature(

2
torchgen/gen.py
View File

@ -1151,7 +1151,7 @@ def compute_cpp_argument_yaml(
arg["default"] = cpp_a.default
return arg
elif isinstance(cpp_a.argument, SelfArgument):
raise AssertionError()
raise AssertionError
elif isinstance(cpp_a.argument, Argument):
return compute_argument_yaml(
cpp_a.argument,